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Pancreatic cancer is molecularly diverse, with few effective therapies. Increased mutation burden and defective DNA repair are associated with response to immune checkpoint inhibitors in several other cancer types. We interrogated 385 pancreatic cancer genomes to define hypermutation and its causes. Mutational signatures inferring defects in DNA repair were enriched in those with the highest mutation burdens. Mismatch repair deficiency was identified in 1% of tumors harboring different mechanisms of somatic inactivation of MLH1 and MSH2. Defining mutation load in individual pancreatic cancers and the optimal assay for patient selection may inform clinical trial design for immunotherapy in pancreatic cancer., (Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background & Aims: Current methods of preoperative staging and predicting outcome following pancreatectomy for pancreatic cancer (PC) are inadequate. We evaluated the utility of multiple biomarkers from distinct biologic pathways as potential predictive markers of response to pancreatectomy and patient survival., Methods: We assessed the relationship of candidate biomarkers known, or suspected, to be aberrantly expressed in PC, with disease-specific survival and response to therapy in a cohort of 601 patients., Results: Of the 17 candidate biomarkers examined, only elevated expression of S100A2 was an independent predictor of survival in both the training (n = 162) and validation sets (n = 439; hazard ratio [HR], 2.19; 95% confidence interval [CI]: 1.48-3.25; P < .0001) when assessed in a multivariate model with clinical variables. Patients with high S100A2 expressing tumors had no survival benefit with pancreatectomy compared with those with locally advanced disease, whereas those without high S100A2 expression had a survival advantage of 10.6 months (19.4 vs 8.8 months, respectively) and a HR of 3.23 (95% CI: 2.39-4.33; P < .0001). Of significance, patients with S100A2-negative tumors had a significant survival benefit from pancreatectomy even in the presence of involved surgical margins (median, 15.7 months; P = .0007) or lymph node metastases (median, 17.4 months; P = .0002)., Conclusions: S100A2 expression is a good predictor of response to pancreatectomy for PC and suggests that high S100A2 expression may be a marker of a metastatic phenotype. Prospective measurement of S100A2 expression in diagnostic biopsy samples has potential clinical utility as a predictive marker of response to pancreatectomy and other therapies that target locoregional disease.

Background & Aims: Pancreatic adenocarcinoma is a most devastating cancer that presents late and is rapidly progressive. This study aimed to identify unique, tissue-specific protein biomarkers capable of differentiating pancreatic adenocarcinoma (PC) from adjacent uninvolved pancreatic tissue (AP), benign pancreatic disease (B), and nonmalignant tumor tissue (NM)., Methods: Tissue samples representing PC (n = 31), AP (n = 44), and B (n = 19) tissue were analyzed on hydrophobic protein chip arrays by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Training models were developed using logistic regression and validated using the 10-fold cross-validation approach., Results: The hydrophobic protein chip array revealed 13 protein peaks differentially expressed between PC and AP (receiver operating characteristic [ROC] area under the curve [AUC], 0.64-0.85), 8 between PC and B (ROC AUC, 0.67-0.78), and 12 between PC and NM tissue (ROC AUC, 0.63-0.81). Logistic regression and cross-validation identified overlapping panels of peaks to develop a training model that distinguished PC from AP (77.4% sensitivity, 84.1% specificity), B (83.9% sensitivity, 78.9% specificity), and NM tissue (58.1% sensitivity, 90.5% specificity). The final panels selected correctly classified 80.6% of PC and 88.6% of AP samples (ROC AUC, 0.92), 93.5% of PC and 89.5% of B samples (ROC AUC, 0.99), and 71.0% of PC and 92.1% of NM samples (ROC AUC, 0.91)., Conclusions: This study used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discover a number of protein panels that can distinguish effectively between pancreatic adenocarcinoma, benign, and adjacent pancreatic tissue. Identification of these proteins will add to our understanding of the biology of pancreatic cancer. Furthermore, these protein panels may have important diagnostic implications.